A series of four-hour nutrient enrichment injections were conducted at Como Creek, CO during the summer of 2018 as part of a collaborative NSF funded research project that is evaluating the hydrologic and stoichiometric conditions that maximize N retention. Each series of enrichment injections consisted of the following treatments:
Grab samples were taken throughout the experiments to determine background concentrations, plateau concentrations and concentrations down the falling limb of the breakthroughcurve. This analysis will determine the uptake length, average distance traveled by a dissolved nutrient before biotic uptake, for nitrogen for each of the four treatments (Tank, Reisinger, and Rosi 2017).
## Setup
knitr::opts_knit$set(root.dir='./..')
knitr::opts_chunk$set(echo=TRUE)
knitr::opts_chunk$set(warning = FALSE)
knitr::opts_chunk$set(message=FALSE)
library(tidyverse)
library(lubridate)
library(xts)
library(dygraphs)
library(ggrepel)
library(knitr)
library(plotly)
library(ggthemes)
library(RColorBrewer)Plateau longitudinal samples were taken every 50 meters throughout the stream reach each day during the plateau portion of the breakthrough curve. These samples represent the highest concentration of added nutrients throughout the experiment. In this step, raw chemistry data is read in, filtered to only include the plateau samples, and flagged for minimum and maximum detection limits. If value is below the minimum detection limit, one half of the detection limit is used as replace value.
## Load Raw Chem Data for Plateau Samples only
chem_grab_plat<- read_csv(('data/in/2018_ComoCreek_WaterChemistry_Mastersheet_Recreated_103019.csv'), skip=7)%>%
mutate(Datetime= mdy_hm(Datetime,tz='MST7MDT'))%>%
mutate(run_date= mdy(run_date))%>%
select(-ID)%>%
mutate(id = rownames(.))%>%
select(
sample = "Sample #" ,
id,
site = Location,
datetime = Datetime,
compartment = Type1,
sample_type = Type2,
rundate = run_date,
df = DF,
Na:SO4_DIL
)%>%
filter(compartment == 'MC') %>%
filter(sample_type %in% c('PLAT LONG', 'PLAT LONG DUP', 'PLAT LONG DIL'))
# lookup table for chem Max/Min
chem_qc= data.frame(var = c('Cl','NO3','PO4'), MaxDL=c(1.7, 5, 7.5), MDL=c(.01, .01, .01))### Create long dataset
chem<-chem_grab_plat %>%
select(sample:SO4)%>%
gather(var, value, Na:SO4) %>%
filter(var %in% c('NO3','Cl','PO4'))%>%
mutate(value = as.numeric(value))%>%
mutate(site = as.numeric(site))%>%
mutate(df = if_else(is.na(df),1,df))%>%
mutate(conc_type = 'final') %>%
mutate(value = if_else(is.na(value), .005, value))
chem_flag<-
chem%>%
mutate(value = value/df)%>%
mutate(conc_type='raw')%>%
left_join(chem_qc)%>%
mutate(MaxDL_flag = if_else(value>MaxDL, 'Yes','No'))%>%
mutate(MDL_flag= if_else(value<MDL, 'Yes','No'))%>%
select(id, var, MaxDL_flag, MDL_flag)
chem_long<-
chem%>%
left_join(chem_flag)%>%
mutate(id = rownames(.))### Define functions to graph data and set color scheme
plotChem <- function (df, choose_param=c( 'Cl', 'NO3', 'PO4')) {
date_title<-date(df$datetime[[1]])
ggplotly(
ggplot(
dplyr::filter(df, var %in% choose_param),
aes(
site,
value,
fill = c(sample_type),
text = paste('date: ',datetime,
'<br> value:',value,
'<br> sample_type:',sample_type,
'<br> rundate:',rundate,
'<br> sample:',sample,
'<br> df:',df,
'<br> MaxDL_flag:',MaxDL_flag,
'<br> MDL_flag:',MDL_flag,
'<br> id:',id
),
group = 1
)
) +
geom_point(shape = 21, size = 3) +
geom_line() +
theme_few() +
scale_fill_manual(values = col_vector) +
facet_wrap( ~ var, ncol = 1, scale = 'free_y')+
labs(title = date_title),
width = 1200,
height = 1000,
tooltip = 'text'
)
}
#create color scheme for chem plots
n <- 40
qual_col_pals = brewer.pal.info[brewer.pal.info$category == 'qual',]
col_vector = unlist(mapply(brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))Graph background longitudinal sample concentrations for the ions of interest (NO3, PO4, and Cl) each day. Visually inspect data for outliers, select which value from different in-lab runs (out of the series of duplicate and dilution runs) and create “good id” vector of chosen datapoints.
# Filter for each injection
chem_grab_nest<-chem_long%>%
mutate(date=date(datetime))%>%
arrange(date)%>%
group_by(date)%>%
nest()
a<-map(chem_grab_nest$data, plotChem)
a[[1]]a[[2]]a[[3]]a[[4]]a[[5]]a[[6]]a[[7]]a[[8]]good_id<-c(75:83, 84:92, 93:109, 161, 163:170, 174, 193:198, 200, 204, 212:214, 218:229, 242, 249, 258:266, 268:276, 278:286, 288:295, 328, 369:377, 379:386, 437:440, 443, 444, 446:448, 458:460, 472:474, 505, 507, 508, 535:543, 545:552, 585, 636:643, 703:705, 715:717, 729:731)
#create dataset of only selected points
chem_clean_long <- chem_long%>%
filter(id %in% good_id)
#double check that Max Detection Limit is vaild
chem_clean_long_flagged<-chem_clean_long%>%
filter(MaxDL_flag== 'Yes')
count(chem_clean_long_flagged)## # A tibble: 1 x 1
## n
## <int>
## 1 8Graph selected plateau longitudinal sample concentrations for the ions of interest (NO3, PO4, and Cl) each day. Use this as a double check before you continue to use these data points in future analysis.
chem_clean_nest<-chem_clean_long%>%
mutate(date=date(datetime))%>%
arrange(date)%>%
group_by(date)%>%
nest()
write_csv(chem_clean_long,"data/in/chem_clean_long.csv" )
b<-map(chem_clean_nest$data, plotChem)
b[[1]]b[[2]]b[[3]]b[[4]]b[[5]]b[[6]]b[[7]]b[[8]]Tank, Jennifer L., Alexander J. Reisinger, and Emma J. Rosi. 2017. “Nutrient Limitation and Uptake.” In Methods in Stream Ecology: Third Edition, 3rd ed., 2:147–71. https://doi.org/10.1016/B978-0-12-813047-6.00009-7.